cardiac health status implementation on mobile phones

8/2/2019 cardiac health status implementation on mobile phones

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CARDIAC HEALTH STATUS

IMPLEMENTATION ON MOBILE PHONES

By-Prasad Pomaji

Abhinav Sharma

Bhagyashri Samanta

Tejashree Chhajed


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Cardio Vascular Diseases

Heart disease or cardiovascular

disease are the class of diseases that

involve the heart or blood vessels.

30 percent of all deaths worldwide,

making it the single leading cause of

death.


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Diagnosis Tools

Electrocardiogram (ECG) is the mostwidely used method for diagnosingcardiovascular disease.

ECG measures the electrical impulsesthat travel through the heart,determining its rate and rhythm.

It can be used to spot coronary problems

such as heart attacks, abnormal heartrhythms, and reduced blood supply tothe heart and electrolyte disturbances.


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Limitations of current diagnosis

methods

Most electrocardiogram machines used

in hospitals and clinics today are

stationary thereby the treatment comes

too late.

Moreover checkups too at hospitals and

clinics have become very expensive.

Todays lifestyle just do not allowpeople to have a spare time, especially

for a routine checkup.(negligence)


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A More Versatile Solution

Cell Phone-BasedMonitoring of ECG data

Features: It collects the ECG data also analyzes it

to detect cardiac abnormalities orpossible cardiovascular conditions.

Low-cost. Minimizes delay which arises by

conventional methods.


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Acquired signal

We study the signal from MIT-BIH database

(Massacheutts Institute of Technology-Beth Israel

Hospital )

Eg. Signal:

Elapsed time ECG ECG

hh:mm:ss.mm (mV) (mV)

0:00.000 -0.207 -0.052


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Overview


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Signal pre-processing

Sampling ofsignal

Noises

Removal of base

line drift

Filtering

Adaptive

filteringtechniques


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Adaptive filtering techniques

Adaptive self tuning filter.

An adaptive self tuning filter is a digital filter with self

adjusting characteristic and in-built flexibility.

Uses LMS algorithm.Bandpass filter.

This filter is a combination of low pass and high pass filter.

Median filter.

It suppress isolated noise without blurring sharp edges.

These algorithms are implemented and results are compared

and best result is chosen.


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QRS detectionMost important waveform.

Is a fundamental algorithm for all

ECG features.

Detection algorithms:

Algorithm based on amplitude and

First derivative only.

Algorithm based on first derivative

only.

Algorithm based on first and

second derivative.

Results of all above algorithms are

compared and a solution is

obtained.


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Feature Extraction

Two main

categories:Morphological

features.

Statistical features.


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Feature Extraction

Morphological features

QRS area

QRS duration

R-R interval

PR interval

R wave amplitude

RT interval

QT segment

ST interval

Statistical features

QRS energy

Auto-correlationcoefficient


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Signal classification

Signal classification is comparing with expert rules.

The extracted features from the acquired signal iscompared with standard rules.


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PROBLEM STATEMENT

Cardiac Health Status Implementation on

Mobile phones.

Feasibility:


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Mathematical Model S= {Q, I, R, F}

Q: Set of Input

I: Initial State R: Intermediate State

F: Final State

Q={x: Set of Input from MIT-BIH database}

I= {A, V, B}

A={x, y: Plot (x,y) Voltage vs Time}

V={x: Adaptive Filtering Technique}

B={x: Base Line Drift Removal}

R= {M, N, O}

M= {R1, R2, R3, R4, R5}

R1={x: QRS Interval Calculation}

R2={x: QT Interval Calculation}

R3={x: ST Segment Calculation}

R4={x:RR Interval Calculation} R5={x:PR Interval Calculation}

N={x: Feature Extraction}

O={x:ANN Signal Classification}

F={x,y: Disease Classification from Z }

Z={x: Ischaemia, Hypoglycaemia, Heart Beat Rate(HBR),

Myocardial Ischaemia}


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UML Diagrams

Usecase

diagram:


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Class diagram


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State Machine diagram


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Sequence Diagram


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Deployment Diagram


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Interaction Diagram


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Test Cases Purpose: To find proper input connection from database.

Expected Output : Find the input and plot the ECG graph.

For Test Case 1:A check is performed to validate that a proper connectionbetween the database and system is maintained to avoid unrealistic values.

Purpose: Noise is introduced into the input making it unreadable.

Expected Output : A proper filtering technique is to be performed for each of the

algorithm.

For Test Case 2:Appropriate action is needed to be taken to avoid noisefrom affecting the systems performance. If the input values cross certain

limitations, the input is skipped to maintain a real time system.

Purpose: To recognize patterns and perform feature extraction and

classification.

Expected Output : Appropriate ECG features are to be studied for different

algorithms.

For Test Case 3: Each pattern of wave values is recorded and studied andcompared with the expert rules.

Purpose: To check efficiency of the system.

Expected Output : The efficiency is checked by employing different algorithms

for input set to generate a more accurate result.

For Test Case 4: System algorithms have been selected to make our system

efficient for optimal and better performance.


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Platform TechnologyThe project deals with implementing ECG system on Mobile phones:

1) Preprocessing:

This techniques will use the electronic components which a filter

uses and all the low frequency and high frequency sound is

removed.

2) Feature Extraction:

Using clustering algorithms .

The technology should provide us with adequate facilities for

parallel processing and finding a high quality solution.

The main focus is on real time processing.

3) Signal classification:

In this step computerized Expert rules are used and classification

of signal is done in order to diagnose the patient.

All the three steps are done on mobile platform using object oriented

languages which a phone uses.


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Project planWaterfall Model :

Requirements specification:

Mobility in systemEasy User Interface

Low Price

Working should be accurate

Should raise an alarm

Design:Hardware specification: This system uses a mobile phone.

User Interface: The user interface should be very easy to use by

any individual.

Price: Low.

Working: The system uses clustering algorithms, Expert rules, databases, internet

and wireless phones to make it a success.

Implementation: Using Object Oriented language C++.

Testing: Unit testing ,System testing

Maintenance :Software U dation time to time.


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Applications

Can be used on all generic mobile phones.

As it has no restricted coverage area hence can be

used within global coverage area.

Can be used to detect all heart related diseases


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Limitations

Security

Security threats

Wireless transmiision threats

Time Lag in processing

In parrallel processing


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Conclusion

Ease of Patients.

Ease of Doctors.

Mobility and Flexibility of device.Error free Proccesing.


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Future Work

Explore the correlation among medical data to

reduce the false positive rate.

We plan to apply the approach to differentmedical contexts.


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References1. Lacramioara Dranca, Alfredo Goni and Arantza Illarramendi , Using decision

trees for real-time ischemia detection, 19th IEEE Symposium on CBMS'06.

2. Cuiwei Li , Chongxun Zheng , Changfeng Tai , Detection of ECG characteristic

points using wavelet transforms, Jan. 1995 IEEE Transactions on Biomedicaltechnology.

3. K. W. Goh, ,J. Lavanya, Y Kim, E. K. Tan and C. B. Soh , A Pda-Based Ecg

Beat Detector For Home Cardiac Care, 2005 IEEE Engineering in Medicine and

Biology.

4. Daniele Apilette and Elena Baralis, Real time analysis of physiological data to

support medical applications, 2009 IEEE transactions on Information technology

in biomedicine.5. Zetao Lin, Yaozheng Ge and Guoliang Tao Algorithm for Clustering Analysis of

ECG Data, Proceedings of the 2005 IEEE, Engineering in Medicine and Biology

27th Annual Conference.

6. Yuliyan Velchev and Ognian Boumbarov, Wavelet Transform Based ECG

Characteristic Points Detector, International Scientific Conference Computer

Science2008.

7. S. S. Mehta and N. S. Lingayat, Detection of P and T-waves in

Electrocardiogram, Proceedings of the World Congress on Engineering andComputer Science 2008.

8. P. Hamilton, Open Source ECG Analysis,Computers in Cardiology,2002 IEEE.

9. Jiapupan and Willis J. Tompkins, A Real-Time QRS Detection Algorithm, IEEE

Transactions on Biomedical Engineering, March 1985.

10. K. W. Goh, J. Lavanya, Y. Kim, E. K. Tan and C. B. Soh, A PDA-Based ECG

Beat Detector for home Cardiac care, Proceedings of the 2005 IEEE,

Engineering in Medicine and Biology 27th Annual Conference.

cardiac health status implementation on mobile phones

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